Data mining is used to help businesses, organisations, researchers and others to support decision making, often when the correct answer is unclear. The development of data warehouses (centralised data storage) has led to the interconnection of multiple databases to form a massive volume of data.
Data mining is about finding patterns and/ or relationships and it does not care why these exist. For example, it is like asking a friend to find a pattern in a page full of numbers. The friend spends a few hours looking and finds one. However, the friend has no idea what the pattern shows only that it is there.
Data mining is an analytical process used to search for consistent patterns and/ or systematic relationships between variables and is validated by applying the detected pattern to a new data set.
The Three Step Approach (old school)
|Initial Exploration||Preparing data|
|Managing the range of data|
|Decide on analytical approach|
|Model Building and Validation||Identify which model provided the best stabilised results (predictive performance)|
|Deployment||Test selected model on new data set|
CRISP-DM Six Step Approach (new school)
- Business Understanding
This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem by creating preliminary plan designed to achieve the objectives.
- Data Understanding
The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
- Data Preparation
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.
In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed.
At this stage in the project you have built a model (or models) that appear to have high quality, from a data analysis perspective. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model, to be certain it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.
Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that the customer can use it. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps. However, even if the analyst will not carry out the deployment effort it is important for the customer to understand up front the actions which will need to be carried out in order to actually make use of the created models.